Description

For a data scientist building predictive models, the following are important:

How good is the model ?

How good is it compared to competing/alternate models?

Is there a way to identify what worked in the models built so far, to leverage it to build something even better?

The stakeholder/end-user who finally uses the output from the model, for whom the ML process is mostly black-box, is concerned with the following:

How to trust the model output?

How to understand the drivers?

How to do what-if analysis?

The unifying theme that could answer most of the above questions is visualization. The biggest challenge is to find a way to visualize the model, the model fitting process and the impact of drivers. This talk summarizes the learnings and key takeaways when communicating model results.